期刊
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
卷 11, 期 14, 页码 5412-5417出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpclett.0c01518
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资金
- National Research Foundation of Korea (NRF) - Ministry of Education [NRF-2020R1C1C1010373]
- KISTI [KSC-2019-CRE0203]
Surface areas of porous materials such as metal-organic frameworks (MOFs) are commonly characterized using the Brunauer-Emmett-Teller (BET) method. However, it has been shown that the BET method does not always provide an accurate surface area estimation, especially for large-surface area MOFs. In this work, we propose, for the first time, a data-driven approach to accurately predict the surface area of MOFs. Machine learning is employed to train models based on adsorption isotherm features of more than 300 diverse structures to predict a benchmark measure of the surface area known as the true monolayer area. We demonstrate that the ML-based methods can predict true monolayer areas significantly better than the BET method, showing great promise for their potential as a more accurate alternative to the BET method in the structural characterization of porous materials.
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